As data analyst’s become more adept at using analytics tools, they are discovering that conventional data warehouse architectures are inhibiting their abilities to analyze the pertinent data, according to an article in TechTarget by David Loshin, president of Knowledge Integrity Inc., a consulting and development services company.
Loshin says there are three main reasons for that – each of which can be addressed by agile data prep tools that let business analysts, data scientists and other end users bypass the data warehouse and do the data prep themselves.
For one thing, the traditional data warehouse is a central repository of integrated data from one or more disparate sources. The data sets housed in this repository have been extracted from internal transaction processing or operational systems for use in reporting on business performance, according to Loshin.
“This limits the scope and types of analyses that can be performed against the data,” he says. Additionally, the extracted data sets are integrated and standardized to align with a predefined data model, designed for dimensional slicing and dicing.
“Doing so filters out information that may be relevant to particular analytics applications,” he adds.
Finally, the IT group typically develops the processes for transforming the data that goes into a data warehouse. And such an approach may not meet the information needs of the analysts who are going to use the data.
A better approach is to enable analysts to work with the data that best meets their needs. And data prep tools can help them do that, according to Loshin.
Business analysts and data scientists can use the data prep tools to access relevant data from disparate systems, blend and cleanse the data for consistency, and define the business rules that govern their use of the information.
The data prep software also gives them more comprehensive and customized views of the pertinent data than they would get from a data warehouse.
Data prep tools offer companies increased analytical flexibility, enabling them to get more out of their data.